@inproceedings{nhan-etal-2026-gradient-descenders,
title = "Gradient Descenders at {D}ialect{S}ent{E}val 2026: A Comparative Study of Pretrained {A}rabic Transformers for Multi-Dialect Sentiment Analysis",
author = "Nhan, Tran Phuoc Thanh and
Le, Trong Thanh and
V{\u{a}}n, Th{\`i}n {\DJ}ặng",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.178/",
pages = "1414--1418",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents the Gradient Descenders system for DialectSentEval Subtask 1 on multi-dialect Arabic sentiment analysis. Because general-purpose models often miss dialectal nuances, we compare three pretrained encoders with distinct linguistic orientations: MARBERTv2, DarijaBERT, and AraBERT. We formulate the task as three-class classification, employing a parameter-efficient head-only adaptation strategy that freezes encoder parameters. Our results show MARBERTv2 outperforms the others, achieving a Macro F1 of 80.11{\%} and Accuracy of 81.90{\%}, securing fifth place overall. These findings emphasize that backbone selection is critical; specifically, broad dialect-oriented pretraining yields the most transferable representations for multi-dialect generalization."
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<abstract>This paper presents the Gradient Descenders system for DialectSentEval Subtask 1 on multi-dialect Arabic sentiment analysis. Because general-purpose models often miss dialectal nuances, we compare three pretrained encoders with distinct linguistic orientations: MARBERTv2, DarijaBERT, and AraBERT. We formulate the task as three-class classification, employing a parameter-efficient head-only adaptation strategy that freezes encoder parameters. Our results show MARBERTv2 outperforms the others, achieving a Macro F1 of 80.11% and Accuracy of 81.90%, securing fifth place overall. These findings emphasize that backbone selection is critical; specifically, broad dialect-oriented pretraining yields the most transferable representations for multi-dialect generalization.</abstract>
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%0 Conference Proceedings
%T Gradient Descenders at DialectSentEval 2026: A Comparative Study of Pretrained Arabic Transformers for Multi-Dialect Sentiment Analysis
%A Nhan, Tran Phuoc Thanh
%A Le, Trong Thanh
%A Văn, Thìn Đặng
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F nhan-etal-2026-gradient-descenders
%X This paper presents the Gradient Descenders system for DialectSentEval Subtask 1 on multi-dialect Arabic sentiment analysis. Because general-purpose models often miss dialectal nuances, we compare three pretrained encoders with distinct linguistic orientations: MARBERTv2, DarijaBERT, and AraBERT. We formulate the task as three-class classification, employing a parameter-efficient head-only adaptation strategy that freezes encoder parameters. Our results show MARBERTv2 outperforms the others, achieving a Macro F1 of 80.11% and Accuracy of 81.90%, securing fifth place overall. These findings emphasize that backbone selection is critical; specifically, broad dialect-oriented pretraining yields the most transferable representations for multi-dialect generalization.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.178/
%P 1414-1418
Markdown (Informal)
[Gradient Descenders at DialectSentEval 2026: A Comparative Study of Pretrained Arabic Transformers for Multi-Dialect Sentiment Analysis](https://aclanthology.org/2026.arabicnlp-sharedtasks.178/) (Nhan et al., ArabicNLP 2026)
ACL